Artificial Intelligence Assisted Smart Self‐Powered Cable Monitoring System Driven by Time‐Varying Electric Field Using Triboelectricity Based Cable Deforming Detection

Abstract Cable monitoring is essential for the prevention of machine malfunctions as machines are operated dynamically. Traditional methods of cable monitoring, conducted through portable or fixed devices, possess the inherent limitations in real‐time damage detection and precise location identification. Herein, a self‐powered, smart cable monitoring system is proposed, utilizing a triboelectric nanogenerator (TENG) as a sensor for the cable and an electric field energy harvester (EFEH) as a power source of the system. Also, the generated electrical outputs from the EFEH are theoretically and experimentally investigated according to the EFEH‐layer numbers, and the optimal number of EFEH‐layers is determined, generating an average electrical power of 2.04 mW. Through hybridization of TENG and EFEH, a synergistic effect is confirmed, resulting in a remarkable 155% enhancement in electrical energy. Consequently, the proposed system is endowed with self‐powered wireless communication capabilities. Additionally, employing a pre‐trained long short‐term memory‐based model, the system can predict the remaining lifespan of the cable with an accuracy rate of 93.7%. Considering these results, the proposed system demonstrates significant potential for industrial cable monitoring applications in the near future.